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Completed

NCT Number: NCT07052773

Clinical Evaluation of the Lung Cancer AI-based Decision Support Tool in Low-Dose Lung CT

The goal of this observational study is to clinically validate the accuracy of an AI-based decision support tool-the Lung Cancer Detection System (LCDS)-for detecting lung nodules in asymptomatic adults aged 50-79 with a history of heavy smoking who underwent low-dose chest CT (LDCT) scans.

The main questions it aims to answer are:

* Can the LCDS accurately detect the presence of solid pulmonary nodules on LDCT scans, as measured by sensitivity and specificity? * How does the LCDS's performance compare to existing AI systems using the Area Under the Curve-Receiver Operating Characteristic (AUC/ROC) Curve?

Researchers will compare the AI-based interpretations to a ground truth established by consensus among radiologists' double-readings to see if the LCDS can accurately classify cases as 'lung nodule presence' or 'lung nodule absence'.

Participants will:

* Have their de-identified LDCT scans (collected between 2018 and 2023) reviewed retrospectively. * Be evaluated through the LCDS tool, which will classify cases based on lung nodule presence.

Contribute to performance evaluation using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and ROC analysis.

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Key information

Conditions

Lung Cancer Screening

Age range

50 year–79 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Assuta Medical Center

Tel Aviv, Israel

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Undergone an LDCT scan between 2018 and 2023, while a diagnosis record exists.
  • Age is between 50-79 years old.
  • History of smoking at least a 20 pack-year smoking history and currently smoke or have quit within the past 15 years.

Exclusion criteria

  • History of lung cancer: Subjects with a previous diagnosis of lung cancer may be excluded to ensure that the study focuses on detecting new cases or evaluating the progression of the disease.
  • Prior lung nodule detection: Individuals who have previously undergone LDCT scans with documented lung nodules that required medical intervention may be excluded to avoid potential confounding factors in the analysis.

Treatment and study plan

Lung Cancer Detection System (LCDS)

Device

An AI-based decision support software designed to detect solid pulmonary nodules on LDCT chest scans. In this study, the LCDS is applied retrospectively to 100 previously acquired LDCT scans, and its performance is compared to a ground truth established by double-read radiologist reports with arbitration.

Primary outcomes

  1. Sensitivity of LCDS for Detection of Solid Pulmonary Nodules

    Time frame: Through study completion, an average of 1 year

    Proportion of true positive cases correctly identified by the AI-based Lung Cancer Detection System (LCDS) out of all subjects with radiologist-confirmed pulmonary nodules (Ground Truth).

  2. Specificity of LCDS for Detection of Solid Pulmonary Nodules

    Time frame: Through study completion, an average of 1 year

    Proportion of true negative cases correctly identified by the LCDS out of all subjects without pulmonary nodules, as defined by the radiologist consensus ground truth.

Secondary outcomes

  1. Area Under the ROC Curve (AUC) for LCDS Performance

    Time frame: Through study completion, an average of 1 year

    The area under the receiver operating characteristic (ROC) curve comparing AI classifications with the radiologist-defined ground truth for nodule detection.

  2. False Positive Rate per Case

    Time frame: Through study completion, an average of 1 year

    The average number of false positive nodule detections made by the Lung Cancer Detection System (LCDS) per LDCT scan. A false positive is defined as a nodule detected by the AI system that was not confirmed by the radiologist-established ground truth.

Sponsors and collaborators

Lead sponsor

Genesis Medical AI

Industry

Registry information

Official study title

Blinded Retrospective Study to Clinically Validate the Accuracy of the Lung Cancer Detection System (LCDS) AI-based Decision Support Tool for Lung Cancer Low-Dose CT

Acronym: GenMedAILCSD1

Important dates

Study start
2023
Primary completion
2024
Study completion
2024
First posted
Jul 7, 2025
Registry last updated
Jul 7, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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